Making large-scale support vector machine learning practical
Advances in kernel methods
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Rules for Anomaly Detection of Hostile Network Traffic
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Estimating the Support of a High-Dimensional Distribution
Neural Computation
International Journal of Computer Vision
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Intrusion Detection Based on Density Level Sets Estimation
NAS '08 Proceedings of the 2008 International Conference on Networking, Architecture, and Storage
Detecting unknown network attacks using language models
DIMVA'06 Proceedings of the Third international conference on Detection of Intrusions and Malware & Vulnerability Assessment
Anagram: a content anomaly detector resistant to mimicry attack
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
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A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an efficient feature selection method for density level-set estimation where optimal kernel mixing coefficients and model parameters are determined simultaneously. Our approach generalizes one-class support vector machines and can be equivalently expressed as a semi-infinite linear program that can be solved with interleaved cutting plane algorithms. The experimental evaluation of the new method on network intrusion detection and object recognition tasks demonstrate that our approach not only attains competitive performance but also spares practitioners from a priori decisions on feature sets to be used.